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  • (@theloviely) Instagram Profile @theloviely
  • What makes your phone capable of interpreting voice messages isn’t beyond the realm of #spectrogram . X axis = time🕰; Y axis = frequency🎙; colors = energy💡 (for each frequency). Each sound would correspond to a certain pattern, like a code. Picture 1️⃣ is me saying “Lovie”, and you can see roughly two syllables. Video 2️⃣ is “I am Lovie” and “我叫Lovie”. Now you can imagine the challenge to decipher #Chinese , since there’s another dimension of intonations. Picture 3️⃣ is a sentence “I just took a shower and I’m ready for bed”. This makes you appreciate the high resolution and robustness required to interpret precisely. #NLP #naturallanguageprocessing #science
  • What makes your phone capable of interpreting voice messages isn’t beyond the realm of #spectrogram. X axis = time🕰; Y axis = frequency🎙; colors = energy💡 (for each frequency). Each sound would correspond to a certain pattern, like a code. 
Picture 1️⃣ is me saying “Lovie”, and you can see roughly two syllables. 
Video 2️⃣ is “I am Lovie” and “我叫Lovie”. Now you can imagine the challenge to decipher #Chinese, since there’s another dimension of intonations. 
Picture 3️⃣ is a sentence “I just took a shower and I’m ready for bed”. This makes you appreciate the high resolution and robustness required to interpret precisely.
#NLP #naturallanguageprocessing #science
  •  12  0 19 October, 2018

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  • (@madevsit) Instagram Profile @madevsit
  • Part 3 Step 7: Named Entity Recognition (NER) : Look at this example sentence below: London is the capital and most populous city of England and the United kingdom. Some of these nouns present real things in the world. For example, “London”, “England” and “United Kingdom” represent physical places on a map. It would be nice to be able to detect that! With that information, we could automatically extract a list of real-world places mentioned in a document using NLP. The goal of Named Entity Recognition, or NER, is to detect and label these nouns with the real-world concepts that they represent. But NER systems aren’t just doing a simple dictionary lookup. Instead, they are using the context of how a word appears in the sentence and a statistical model to guess which type of noun a word represents. A good NER system can tell the difference between “Brooklyn Decker” the person and the place “Brooklyn” using context clues. Step 8: Coreference Resolution : English is full of pronouns — words like he, she, and it. These are shortcuts that we use instead of writing out names over and over in each sentence. With coreference information combined with the parse tree and named entity information, we should be able to extract a lot of information out of this document! Coreference resolution is one of the most difficult steps in our pipeline to implement. It’s even more difficult than sentence parsing. Summary of an article about nlp by @ageitgey in @medium Tags : #ai #artificialintelligence #artificial_intelligence #computer #computerscience #nlp #naturallanguageprocessing #summary #natural_language_processing #machinelearning #machine_learning #article #medium #tech #technology #computerengineering #madevsit #neuralnetworks #neuralnetworking
  • Part 3

Step 7: Named Entity Recognition (NER) :
Look at this example sentence below:
London is the capital and most populous city of England and the United kingdom.
Some of these nouns present real things in the world. For example, “London”, “England” and “United Kingdom” represent physical places on a map.
It would be nice to be able to detect that! With that information, we could automatically extract a list of real-world places mentioned in a document using NLP.
The goal of Named Entity Recognition, or NER, is to detect and label these nouns with the real-world concepts that they represent.
But NER systems aren’t just doing a simple dictionary lookup. Instead, they are using the context of how a word appears in the sentence and a statistical model to guess which type of noun a word represents. A good NER system can tell the difference between “Brooklyn Decker” the person and the place “Brooklyn” using context clues.

Step 8: Coreference Resolution :
English is full of pronouns — words like he, she, and it. These are shortcuts that we use instead of writing out names over and over in each sentence.
With coreference information combined with the parse tree and named entity information, we should be able to extract a lot of information out of this document!
Coreference resolution is one of the most difficult steps in our pipeline to implement. It’s even more difficult than sentence parsing.

Summary of an article about nlp by @ageitgey in @medium

Tags :
#ai #artificialintelligence #artificial_intelligence #computer #computerscience #nlp #naturallanguageprocessing #summary #natural_language_processing
#machinelearning #machine_learning  #article #medium #tech #technology #computerengineering
#madevsit #neuralnetworks
#neuralnetworking
  •  27  1 14 October, 2018
  • (@madevsit) Instagram Profile @madevsit
  • Part 2 Step 4: Text Lemmatization : In English (and most languages), words appear in different forms. When working with text in a computer, it is helpful to know the base form of each word so that you know that both sentences are talking about the same concept. In NLP, we call finding this process lemmatization — figuring out the most basic form or lemma of each word in the sentence. The same thing applies to verbs. Lemmatization is typically done by having a look-up table of the lemma forms of words based on their part of speech and possibly having some custom rules to handle words that you’ve never seen before. Step 5: Identifying Stop Words : Next, we want to consider the importance of a each word in the sentence. English has a lot of filler words that appear very frequently Some NLP pipelines will flag them as stop words. Stop words are usually identified by just by checking a hardcoded list of known stop words. But there’s no standard list of stop words that is appropriate for all applications. The list of words to ignore can vary depending on your application. Step 6: Dependency Parsing : The next step is to figure out how all the words in our sentence relate to each other. The goal is to build a tree that assigns a single parent word to each word in the sentence. The root of the tree will be the main verb in the sentence. Just like how we predicted parts of speech earlier using a machine learning model, dependency parsing also works by feeding words into a machine learning model and outputting a result. Step 6b: Finding Noun Phrases : sometimes it makes more sense to group together the words that represent a single idea or thing. We can use the information from the dependency parse tree to automatically group together words that are all talking about the same thing. Summary of an article about nlp by @ageitgey in @medium Tags : #ai #artificialintelligence #artificial_intelligence #computer #computerscience #nlp #naturallanguageprocessing #summary #natural_language_processing #machinelearning #machine_learning #article #medium #tech #technology #computerengineering #madevsit #neuralnetworks #neuralnetworking
  • Part 2

Step 4: Text Lemmatization :
In English (and most languages), words appear in different forms.
When working with text in a computer, it is helpful to know the base form of each word so that you know that both sentences are talking about the same concept.
In NLP, we call finding this process lemmatization — figuring out the most basic form or lemma of each word in the sentence.
The same thing applies to verbs.
Lemmatization is typically done by having a look-up table of the lemma forms of words based on their part of speech and possibly having some custom rules to handle words that you’ve never seen before.

Step 5: Identifying Stop Words :
Next, we want to consider the importance of a each word in the sentence. English has a lot of filler words that appear very frequently
Some NLP pipelines will flag them as stop words.
Stop words are usually identified by just by checking a hardcoded list of known stop words. But there’s no standard list of stop words that is appropriate for all applications. The list of words to ignore can vary depending on your application.

Step 6: Dependency Parsing :
The next step is to figure out how all the words in our sentence relate to each other.
The goal is to build a tree that assigns a single parent word to each word in the sentence. The root of the tree will be the main verb in the sentence.
Just like how we predicted parts of speech earlier using a machine learning model, dependency parsing also works by feeding words into a machine learning model and outputting a result.

Step 6b: Finding Noun Phrases :
sometimes it makes more sense to group together the words that represent a single idea or thing. We can use the information from the dependency parse tree to automatically group together words that are all talking about the same thing.

Summary of an article about nlp by @ageitgey in @medium

Tags :
#ai #artificialintelligence #artificial_intelligence #computer #computerscience #nlp #naturallanguageprocessing #summary #natural_language_processing
#machinelearning #machine_learning  #article #medium #tech #technology #computerengineering
#madevsit #neuralnetworks
#neuralnetworking
  •  22  1 14 October, 2018
  • (@madevsit) Instagram Profile @madevsit
  • Part 1 Natural Language Processing, or NLP, is the sub-field of AI that is focused on enabling computers to understand and process human languages. Doing anything complicated in machine learning usually means building a pipeline. The idea is to break up your problem into very small pieces and then use machine learning to solve each smaller piece separately. Then by chaining together several machine learning models that feed into each other, you can do very complicated things. Step 1: Sentence Segmentation : The first step in the pipeline is to break the text apart into separate sentences. Coding a Sentence Segmentation model can be as simple as splitting apart sentences whenever you see a punctuation mark. But modern NLP pipelines often use more complex techniques that work even when a document isn’t formatted cleanly. Step 2: Word Tokenization : The next step in our pipeline is to break this sentence into separate words or tokens. Tokenization is easy to do in English. We’ll just split apart words whenever there’s a space between them. Step 3: Predicting Parts of Speech for Each Token : Next, we’ll look at each token and try to guess its part of speech — whether it is a noun, a verb, an adjective and so on. Knowing the role of each word in the sentence will help us start to figure out what the sentence is talking about. We can do this by feeding each word (and some extra words around it for context) into a pre-trained part-of-speech classification model. Summary of an article about nlp by @ageitgey in @medium Tags : #ai #artificialintelligence #artificial_intelligence #computer #computerscience #nlp #naturallanguageprocessing #summary #natural_language_processing #machinelearning #machine_learning #article #medium #tech #technology #computerengineering #madevsit #neuralnetworks #neuralnetworking
  • Part 1

Natural Language Processing, or NLP, is the sub-field of AI that is focused on enabling computers to understand and process human languages.

Doing anything complicated in machine learning usually means building a pipeline. The idea is to break up your problem into very small pieces and then use machine learning to solve each smaller piece separately. Then by chaining together several machine learning models that feed into each other, you can do very complicated things.

Step 1: Sentence Segmentation :
The first step in the pipeline is to break the text apart into separate sentences.
Coding a Sentence Segmentation model can be as simple as splitting apart sentences whenever you see a punctuation mark. But modern NLP pipelines often use more complex techniques that work even when a document isn’t formatted cleanly.

Step 2: Word Tokenization :
The next step in our pipeline is to break this sentence into separate words or tokens.
Tokenization is easy to do in English. We’ll just split apart words whenever there’s a space between them.

Step 3: Predicting Parts of Speech for Each Token :
Next, we’ll look at each token and try to guess its part of speech — whether it is a noun, a verb, an adjective and so on. Knowing the role of each word in the sentence will help us start to figure out what the sentence is talking about.
We can do this by feeding each word (and some extra words around it for context) into a pre-trained part-of-speech classification model.

Summary of an article about nlp by @ageitgey in @medium

Tags :
#ai #artificialintelligence #artificial_intelligence #computer #computerscience #nlp #naturallanguageprocessing #summary #natural_language_processing
#machinelearning #machine_learning  #article #medium #tech #technology #computerengineering
#madevsit #neuralnetworks
#neuralnetworking
  •  17  0 14 October, 2018

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